An Optimized Byzantine Fault Tolerance Algorithm for Medical Data Security

Authors:
Open Access
Publisher website
Google Scholar
Abstract
Medical data are an intangible asset and an important resource for the entire society. The mining and application of medical data can generate enormous value. Currently, medical data management is mostly centralized and heavily relies on central servers, which are prone to malfunctions or malicious attacks, making it difficult to form a consensus among multiple parties and achieve secure sharing. Blockchain technology offers a solution to enhance medical data security. However, in medical data security sharing schemes based on blockchain, the widely adopted Practical Byzantine Fault-Tolerant (PBFT) algorithm encounters challenges, including intricate communication, limited scalability, and the inability to dynamically add or remove nodes. These issues make it challenging to address practical requirements effectively. In this paper, we implement an efficient and scalable consensus algorithm based on the PBFT consensus algorithm, referred to as Me-PBFT, which is more suitable for the field of medical data security. First, we design a reputation evaluation model to select more trusted nodes to participate in the system consensus, which is implemented based on a sigmoid function with adjustable difficulty. Second, we implement the division of node roles to construct a dual consensus layer structure. Finally, we design a node dynamic join and exit mechanism on the overall framework of the algorithm. Analysis shows that compared to PBFT and RAFT, ME-PBFT can reduce communication complexity, improve fault tolerance, and have good scalability. It can meet the need for consensus and secure sharing of medical data among multiple parties.
Funding Information
  • Yunnan Key Laboratory of Blockchain Application Technology (202105AG070005, YNB202301, 202303AC100003, 62271234, PBD2022-16, LJGXCG2022-054)
  • Project of Science and Technology Major Project of Yunnan Province (202105AG070005, YNB202301, 202303AC100003, 62271234, PBD2022-16, LJGXCG2022-054)
  • National Natural Science Foundation of China (202105AG070005, YNB202301, 202303AC100003, 62271234, PBD2022-16, LJGXCG2022-054)
  • Open Foundation of State Key Laboratory of Public Big Data (Guizhou University) (202105AG070005, YNB202301, 202303AC100003, 62271234, PBD2022-16, LJGXCG2022-054)
  • Double First-Class Project for Collaborative Innovation Achievements in Disciplines Construction in Heilongjiang Province (202105AG070005, YNB202301, 202303AC100003, 62271234, PBD2022-16, LJGXCG2022-054)
  • Advanced Programs of Heilongjiang Province for the overseas scholars (202105AG070005, YNB202301, 202303AC100003, 62271234, PBD2022-16, LJGXCG2022-054)
  • Fundamental Research Funds for Heilongjiang Universities (202105AG070005, YNB202301, 202303AC100003, 62271234, PBD2022-16, LJGXCG2022-054)

This publication has 27 references indexed in Scilit: